from uniswap_fee_and_divergence import *

# Import pickle file
import pickle 

with open('backtest_results.pickle', 'rb') as handle:
    results = pickle.load(handle)

import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default='notebook'

def analyse_results(results):
    # Initialize empty DataFrame to hold summary stats
    summary_stats = pd.DataFrame()
    fig = go.Figure()

    # Loop through the results
    for result in results:
        # Get general info
        range_pct = result["range_pct"]
        is_hedged = result["is_hedged"]
        backtester = result["backtester"]

        # Get backtest DataFrame
        backtest_df = backtester.backtest_df

        # Calculate key stats
        fees_generated = backtest_df["fee"].sum()
        losses_due_to_divergence = backtest_df["divergence"].sum()
        pnl_total_with_fees = backtest_df.groupby("id")["pnl_total_with_fees"].last().sum()
        roi = backtester.roi
        max_dd = backtester.max_dd
        # Append stats to summary DataFrame
        summary_stats = pd.concat([summary_stats, pd.DataFrame({
            "Range Percentage": range_pct,
            "Is Hedged": is_hedged,
            "Number of positions": len(backtest_df.id.unique()),
            "Total Fees Generated": fees_generated,
            "Losses Due to Divergence": losses_due_to_divergence,
            "Total PNL with Fees": pnl_total_with_fees,
            "ROI": roi,
            "DD": max_dd,
            "ID": results.index(result)
        }, index=[0])])

        # Generate line for each backtest
        fig.add_trace(go.Scatter(
            x=backtest_df.index,
            y=backtest_df['net_usd_capital'],
            mode='lines',
            name=f'{range_pct}%, {"Hedged" if is_hedged else "Unhedged"}',
            #hovertemplate = '<b>Capital</b>: %{y:.2f}<extra></extra>'
        ))

    # Edit layout
    fig.update_layout(title='Backtest Capital Over Time for Different Configurations',
                      height=600,
                    xaxis_title='Time',
                    yaxis_title='Net USD Capital',
                    hovermode="x unified")
    
    fig.show()
    
    # Set index to the configurations for easier referencing
    summary_stats.set_index(["Range Percentage", "Is Hedged"], inplace=True)

    return summary_stats

summary_stats = analyse_results(results)

# Displaying the summary sorted by ROI
summary_stats.sort_values(by='ROI', ascending=False)

		Number of positions	Total Fees Generated	Losses Due to Divergence	Total PNL with Fees	ROI	DD	ID
Range Percentage	Is Hedged
0.25	True	5427	3.045300e+06	-12.967646	367941.401915	157.673319	0.456666	24
	False	5427	3.045300e+06	-19.306082	95305.687183	95.305687	0.526607	25
0.50	True	3349	1.522650e+06	-11.060791	175150.090926	80.303426	0.488346	22
0.75	True	2281	1.015100e+06	-10.327435	113217.506109	51.234421	0.540954	20
1.00	True	1658	7.613249e+05	-9.445027	89710.865240	41.840826	0.575305	18
0.50	False	3349	1.522650e+06	-16.769627	30933.574210	30.933574	0.608637	23
2.00	True	694	3.806624e+05	-4.406666	45732.541752	14.829102	0.625625	16
0.75	False	2281	1.015100e+06	-15.555946	7401.576860	7.401577	0.677577	21
3.00	True	397	2.537750e+05	-9.343309	28719.668960	0.909471	0.634923	14
1.00	False	1658	7.613249e+05	-14.536074	678.959142	0.678959	0.700314	19
4.00	True	270	1.903312e+05	-15.913529	20886.601208	-4.408404	0.658010	12
6.00	True	131	1.268875e+05	-24.717941	15276.492192	-8.258188	0.635138	8
5.00	True	187	1.522650e+05	-14.110463	14447.637649	-10.557003	0.634995	10
7.00	True	105	1.087607e+05	-32.574201	8627.287566	-15.938392	0.636237	6
9.00	True	66	8.459165e+04	14.890108	9694.161175	-16.312923	0.580023	2
2.00	False	694	3.806624e+05	-10.881516	-18693.215035	-18.693215	0.711620	17
8.00	True	92	9.516561e+04	-26.127067	5298.602725	-20.605073	0.623922	4
10.00	True	63	7.613249e+04	-24.565869	1048.318563	-24.396788	0.604139	0
3.00	False	397	2.537750e+05	-15.821403	-28648.366485	-28.648366	0.699935	15
4.00	False	270	1.903312e+05	-22.023441	-30425.783004	-30.425783	0.724144	13
6.00	False	131	1.268875e+05	-32.427186	-31702.711884	-31.702712	0.677762	9
5.00	False	187	1.522650e+05	-22.444481	-35212.912365	-35.212912	0.676395	11
9.00	False	66	8.459165e+04	-7.029798	-36165.767882	-36.165768	0.605812	3
7.00	False	105	1.087607e+05	-39.596866	-37328.381575	-37.328382	0.661893	7
8.00	False	92	9.516561e+04	-38.529036	-40540.446266	-40.540446	0.654174	5
10.00	False	63	7.613249e+04	-36.500826	-43285.531112	-43.285531	0.603830	1

Uniswap V3 Strategy Backtest Results

Executive Summary

This internal document presents the result of our backtest on a Uniswap V3 strategy for the ETH/USDC pool with the aim to assess the impact of different configurations on performance, specifically focusing on the range and whether a hedging strategy was implemented.

Our findings indicate that both range percentage and hedging are vital factors influencing the strategy's performance. The key results of our backtest are summarized below:

• The configuration that achieved the highest ROI (157.67%) had a range percentage of 0.25 and utilized hedging. This configuration also had the lowest drawdown (45.7%), indicating a relatively lower risk compared to other configurations.

• Overall, configurations with hedging resulted in a better ROI than those without. For example, when a range of 0.25% was used, the hedged strategy achieved a significantly higher ROI (157.67%) compared to the unhedged strategy (95.31%). This suggests that hedging could effectively offset some of the losses due to the divergence, leading to better overall performance.

• Our analysis also shows that a larger range doesn't necessarily equate to better performance. The configuration with the highest range (10%) had one of the lowest ROIs, whether hedged (-24.40%) or unhedged (-43.29%). This could be due to the fact that larger ranges may result in lower concentrated liquidity multipler, thus generating fewer fees.

• Conversely, a smaller range, specifically 0.25%, achieved the best results in both hedged and unhedged scenarios. This suggests that a more frequent trading approach could be advantageous, given the fee structure of Uniswap V3.

• As expected, hedging helped decrease the directional risk. For each range, hedged strategies consistently exhibited a lower drawdown than their unhedged counterparts. This clearly indicates the risk-mitigation benefit of hedging.

Based on these findings, we suggest further exploration of the following additional strategies to increase return and decrease directional risks:

1. Adjusting the range more dynamically: Instead of sticking to a single static range for the entire duration, the strategy could adjust its range based on market conditions. For example, it could use a larger range in more volatile conditions and a smaller one in less volatile conditions.

2. Incorporating predictive models: Machine learning models could be utilized to predict the future price of ETH and adjust the range or the hedging strategy accordingly.

3. Implementing stop-loss rules: To further control risk, the strategy could close the position and hedge it once the loss reaches a certain threshold.

We hope these insights are helpful in refining our Uniswap V3 strategy. We recommend conducting additional backtests incorporating the strategies suggested above for further optimization.

Latest version of the nalysis notebook can be found here:

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